Welcome to gtrans-equilibrium! This project examines how many steps it takes for a language to reach it's translational equilibrium with the help of Google Translate.
README: Remember to open terminal or command prompt and get these packages by entering:
pip install google_trans_new
pip install BeautifulSoup
pip install pandas
pip install numpy
pip install matplotlib
pip install wordcloud
After cloning the repo, open the translate.py and at line 33 where the translator is instantiated:
translator = google_translator(url_suffix="cn", timeout=5)
change the url_suffix parameter to "com", and then open google_translate_equilibrium.ipynb to run the code cells in order and see the results.
The grabbed_text.txt and output_grabbed_text.txt files should change upon executing the above for your analysis.
Since the program sprang off of TranslationParty, the "hot parties" section is where all of the data comes from. The text is scraped by the party_scraper.py file where the requests and BeautifulSoup packages were used to find all divs of hot parties, their respective href links. The div id was found from inspecting the website elements, and since each "party" has its own caption and href link, it was easy to use the getText() on the href links that was found by find(id="hotparties").find_all("a"). If you wish to change the data source, change the request URL in party_scraper.py and go inspect the source's elements that you intend to use.
First, to view the visuals, click here
The scatterplot can be created with two data lists and using the matplotlib.pyplot.scatter function to create a scatter plot. Note that in this implementation the x axis are variable names instead of values. Only the y axis represent values. Then all that's left are just title, xlabel and ylabel, and showing the plot with show(). More parameters can be found at the documentation: https://matplotlib.org/stable/contents.html.
The Wordcloud can be created with a string representing the text and using the wordcloud.WordCloud().generate(text), in the most basic sense. A matplotlib.pyplot is needed again to show the image with imshow() and turning the axis off with axis("off"). A wordcloud can be created as simple as that, but more variations can be achieved as documented here: http://amueller.github.io/word_cloud.
Lastly, the table visual is achieved with three lists of data and converting lists into a DataFrame from pandas. A column need to be set for the table to have overhead titles, and the DataFrame can then be displayed as shown in the essay.